使用bsts包中的函数bsts使用MCMC进行贝叶斯时间序列分析

如何解决使用bsts包中的函数bsts使用MCMC进行贝叶斯时间序列分析

问题：

我有一个称为FID的数据框（如下所示），其中包含两列，分别表示年和月，以及Sighting_Frequency。

数据框包含2015年至2017年之间的3年观察数据，表明我有36个月的数据。我想按照以下教程，使用 bsts包中的 bsts（）函数（请参见下面的R代码），通过MCMC运行贝叶斯时间序列分析。但是，由于我不断收到以下错误消息，因此我在运行模型时遇到问题：-

    Error in .FormatBstsDataAndOptions(family,response,predictors,model.options,: 
  all(abs(response - as.integer(response)) < 1e-08,na.rm = TRUE) is not TRUE

如果这是可能的，我想知道是否有人可以提供建议，因为我正在努力寻找解决方案，而我不是高级R编码器。我研究了许多教程，将问题放在R Studio Facebook页面上，并且阅读了作者的用户指南。

如果有人可以提供帮助，我将非常感激。

非常感谢。

教程

https://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/?fbclid=IwAR1q6QD5j6AW21FY2_gqDEq-bwBKDJNtg9alKm3bDytzS51w-dVkDZMdbT4

R代码

##Open packages for the time series analysis

library(lubridate)
library(bsts)
library(dplyr)
library(ggplot2)

* 500 MCMC draws.
* Use 2015 as the holdout period.
* Trend and seasonality.
* Forecast created by averaging across the MCMC draws. 
* Credible interval generated from the distribution of the MCMC draws.
* Discarding the first MCMC iterations (burn-in).
* Using a log transformation to make the model multiplicative

##Produce a time series analysis
myts <- ts(BSTS_Dataframe,start=c(2015,1),end=c(2017,12),frequency=12)

# subset the time series (Jan 2015 to December 2017)
x <- window(myts,01),12))
y <- log(x)

### Run the bsts model
ss <- AddLocalLinearTrend(list(),y)
ss <- AddSeasonal(ss,y,nseasons = 15)
bsts.model <- bsts(y,state.specification = ss,family = "poisson",niter = 500,ping=0,seed=2015)

##Error message

Error in .FormatBstsDataAndOptions(family,na.rm = TRUE) is not TRUE

FID数据框

   structure(list(Year = structure(1:32,.Label = c("2015-01","2015-02","2015-03","2015-04","2015-05","2015-08","2015-09","2015-10","2015-11","2015-12","2016-01","2016-02","2016-03","2016-04","2016-05","2016-07","2016-08","2016-09","2016-10","2016-11","2016-12","2017-01","2017-02","2017-03","2017-04","2017-05","2017-07","2017-08","2017-09","2017-10","2017-11","2017-12"
),class = "factor"),Sightings_Frequency = c(36L,28L,39L,46L,5L,22L,10L,15L,8L,33L,29L,31L,23L,9L,40L,41L,30L,44L,37L,42L,20L,7L,27L,35L,43L,38L)),class = "data.frame",row.names = c(NA,-32L
))

解决方法

如果我将poisson与您的数据一起使用，也会出错。

myts2 <- ts(BSTS_Dataframe$Sightings_Frequency,start=c(2015,1),end=c(2017,12),frequency=12)

x <- window(myts2,01),12))
y <- log(x)

### Run the bsts model
ss <- AddLocalLinearTrend(list(),y)
ss <- AddSeasonal(ss,y,nseasons = 3)
# bsts.model <- bsts(y,state.specification = ss,family = "poisson",niter = 2,ping=0,seed=1234)
bsts.model <- bsts(y,family = "logit",niter = 100,ping = 0,seed = 123)
plot(bsts.model)
plot(bsts.model)